Text-to-image diffusion models can create realistic images based on input
texts. Users can describe an object to convey their opinions visually. In this
work, we unveil a previously unrecognized and latent risk of using diffusion
models to generate images; we utilize emotion in the input texts to introduce
negative contents, potentially eliciting unfavorable emotions in users.
Emotions play a crucial role in expressing personal opinions in our daily
interactions, and the inclusion of maliciously negative content can lead users
astray, exacerbating negative emotions. Specifically, we identify the
emotion-aware backdoor attack (EmoAttack) that can incorporate malicious
negative content triggered by emotional texts during image generation. We
formulate such an attack as a diffusion personalization problem to avoid
extensive model retraining and propose the EmoBooth. Unlike existing
personalization methods, our approach fine-tunes a pre-trained diffusion model
by establishing a mapping between a cluster of emotional words and a given
reference image containing malicious negative content. To validate the
effectiveness of our method, we built a dataset and conducted extensive
analysis and discussion about its effectiveness. Given consumers' widespread
use of diffusion models, uncovering this threat is critical for society.

使用情感文本触发恶意负面内容生成图像的基于文本到图像扩散模型的情感感知背门攻击（EmoAttack）及其解决方法 EmoBooth。